Long-term effects of integrated cognitive behavioral therapy for chronic pain: A qualitative and quantitative study
Bibliographic record
Abstract
Cognitive behavioral therapy (CBT) is known to improve chronic pain management. However, past studies revealed only small to moderate benefits in short-term results, and long-term follow-up studies are lacking. This study aimed to follow an integrated CBT program's effectiveness 1.5 years after its completion. This observational study was the follow-up on the data collected from our CBT sessions conducted under 3 different studies in 2018 to 2019. Seven assessment items (Numerical Rating Scale, Pain Catastrophizing Scale [PCS], Pain Disability Assessment Scale [PDAS], Patient Health Questionnaire-9 items, Generalized Anxiety Disorder 7, European quality of life 5-dimensions 5-level, and Beck Depression Inventory [BDI]) were statistically analyzed. Thematic analysis was conducted in semi structured interviews. PCS ( F = 6.52, P = .003), PDAS ( F = 5.68, P = .01), European quality of life 5-dimensions 5-level ( F = 3.82, P = .03), and BDI ( F = 4.61, P = .01) exhibited significant changes ( P < .05), confirmed by pairwise t test, revealing a moderate to large effect size. From post-treatment to follow-up, all scores showed no significant changes ( P > .1). In the qualitative study, the analysis revealed 3 subthemes: "Autonomy," "Understanding of yourself and pain," and "Acceptance of pain." Our study suggests that integrated CBT may reduce the scores of PCS, PDAS and BDI, and this effect lasts for at least 1 year. Identified themes support the relevance of mitigative factors in managing chronic pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".